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We are delighted to announce that MingYu Lu has won the Distinguished Paper award at the AMIA 2020 Annual Symposium! MingYu worked under the guidance of Dr. Li-wei Lehman where they evaluated the safety and robustness of machine learning models for clinical treatment decision making in their paper titled 'Is Deep Reinforcement Learning Ready for Practical Applications in Healthcare? A Sensitivity Analysis of Duel-DDQN for Hemodynamic Management in Sepsis Patients'. Their work systematically explored the sensitivity of a deep reinforcement learning (DRL) technique for sepsis treatment, and uncovered several important areas of caution in adopting DRL in a healthcare setting.

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We are delighted to announce that MIMIC-IV has been published on PhysioNet. MIMIC-IV, the latest version of MIMIC, is a database comprising comprehensive clinical information on hospital stays for patients admitted to a tertiary academic medical center in Boston, MA, USA. Major changes from MIMIC-III include: (1) a modular structure that links core hospital data to multiple data sources, including chest x-ray images; (2) an approach to date shifting that provides approximate year of admission; (3) new sources of data; such as the electronic medicine administration record.

The dataset is available from PhysioNet, and access is managed in the same way as MIMIC-III. If you already have access to MIMIC-III, then you will be granted access after signing the Data Use Agreement. New users will need to complete the credentialing process first (see: for more details).

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The MIT Laboratory for Computational Physiology is seeking a Post-Doctoral Associate to conduct independent research in health care informatics. The Laboratory is an NIH-supported multi-disciplinary group of clinicians, data scientists and engineers that produced the publicly distributed and growing MIMIC database. It is a rich and open research resource that supports signal processing and machine learning research leading to new knowledge and patient-specific prognostic and therapeutic guidance for critical care. The Postdoc will contribute to the design and management of the current and future MIMIC databases, and will conduct multidisciplinary original research together with clinicians. The position provides the opportunity to interact with a world-class laboratory comprised of engineers, mathematicians and clinical staff working at the frontiers of translational medicine and advanced research in the domain of critical care informatics and machine learning.

The Research Fellow will both contribute to ongoing research projects and propose innovative new projects suitable for research grant funding. The ideal candidate has a doctoral degree in science or engineering, or a related discipline to assure high level understanding of the research environment. Experience in relational database development and administration is important, ideally in a medical environment. Competence and experience in a subset of the following is expected: Linux, Python, data management. Knowledge of medical terminology is desirable. Strong interpersonal and communication skills are essential. For more details, send a letter of interest and CV to Prof. Roger Mark at: rgmark AT

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A journal article describing the MIMIC-CXR database was recently published in Scientific Data. The article provides detail regarding the collection, curation, and processing done in order to create the database. The article is open access and available online [1].

The database has also been preprocessed into compressed JPG format images, which have been made available on PhysioNet as the MIMIC-CXR-JPG Database. The database includes labels extracted from the free-text reports using publicly available tools.

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The MIT Laboratory for Computational Physiology is seeking a Research Software Engineer to support and undertake projects focused on improving patient care. Joining a team of data scientists and clinicians, the Engineer will help to manage the system network, write code for research studies, and initiate and develop research software.

The position would be a good fit for someone with a bachelor’s or master’s degree in a technical subject such as biomedical engineering, physics, computer science, or equivalent experience. For more details, please see the job posting on the MIT website or contact us directly if you have questions.

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The MIMIC-III database is now available on two major cloud platforms: Google Cloud Platform (GCP) and Amazon Web Services (AWS). To access the data on the cloud, simply add the relevant cloud identifier to your PhysioNet profile. Further instructions are available on the MIMIC-III website.

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